As learning analytics use grows across U.S. colleges and universities, so does the need to discuss the plans, purposes, and paths for the data collected via learning analytics. More specifically, students, faculty, and others who are impacted by learning analytics use should have more information about their campus' learning analytics practices…

Since its inception in 2011, Learning Analytics has matured and expanded in terms of reach (e.g., primary and K-12 education) and in having access to a greater variety, volume and velocity of data (e.g. collecting and analyzing multimodal data). Its roots in multiple disciplines yield a range and richness of theoretical influences resulting in an…

Central to the institutionalization of learning analytics is the need to understand and improve student learning. Frameworks guiding the implementation of learning analytics flow from and perpetuate specific understandings of learning. Crucially, they also provide insights into how learning analytics acknowledges and positions itself as entangled…

This article maps considerations of inclusiveness and support for students with disabilities by reviewing articles within the field of learning analytics. The study involved a PRISMA-informed systematic review of two popular digital libraries, namely Clarivate's Web of Science, and Elsevier's Scopus for peer-reviewed journal articles and…

Engagement is critical in learning, including computer-supported collaborative learning (CSCL). Previous studies have mainly measured engagement using students' self-reports which usually do not capture the learning process or the interactions between group members. Therefore, researchers advocated developing new and innovative engagement…

Helping students gradually develop collective knowledge is critical but generally faces great challenges. Employing a quasi-experimental design, this study investigated the impacts and mechanisms of analytics-supported reflective assessment on the collective knowledge advancement of undergraduates. The experimental group (n = 55) engaged in…

This paper presents an analysis of learning analytics practices which aimed to achieve personalised learning. It addresses the need for a systematic analysis of the increasing amount of practices of learning analytics which are targeted at personalised learning. The paper summarises and highlights the characteristics and trends in relevant…

The purpose of this mixed-methods study was to explore factors affecting faculty members' motivation to use learning analytics (LA) to improve their teaching. In the quantitative phase, 107 faculty members completed an online survey about their motivation to use LA. The results showed that cost, utility, attainment value, and competence all…

One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models' excessive dependence on the low-level…

Studies in learning analytics (LA) have garnered positive findings on learning improvement and advantages for informing course design. However, little is known about instructional designers' perception and their current state of LA-related adoption. This qualitative study explores the perception of instructional designers in higher education…

This research examined the effect of learning analytics (LA) on students' metacognitive awareness and academic achievement in an online learning environment. In this study, a mixed methods approach was used and applied as a quasi-experimental design. The results of LA were sent to students weekly in LA group (experimental group) via learning…

Learning analytics tools are becoming commonplace in educational technologies, but extant student privacy issues remain largely unresolved. It is unknown whether faculty care about student privacy and see privacy as valuable for learning. The research herein addresses findings from a survey of over 500 full-time higher education instructors. In…

This paper reports a comprehensive review of literature on personalised learning in STEM and STEAM (or STE(A)M) education, which involves the disciplinary integration of Science, Technology, Engineering, and Mathematics, as well as Arts. The review covered the contexts of STE(A)M education where personalised learning was adopted, the objectives of…

Predicting students' learning outcomes is one of the main topics of interest in the area of Educational Data Mining and Learning Analytics. To this end, a plethora of machine learning methods has been successfully applied for solving a variety of predictive problems. However, it is of utmost importance for both educators and data scientists to…

Group formation is a critical factor which influences collaborative processes and performances in computer-supported collaborative learning (CSCL). Automatic grouping has been widely used to generate groups with heterogeneous attributes and to maximize the diversity of students' characteristics within a group. But there are two dominant challenges…